Year: 2024
Type: conference-paper
Abstract: In the paper, personalized results are presented in the methodology for monitoring information security based on voice authentication. Integration of sound preprocessing and Machine Learning techniques for feature extraction, training, and validation of classification models has been implemented. The objects of research are staked mixed-test voice profiles. Classifies were selected with quantitative evaluation under a threshold of 90.00% by Naive Bayes and Discriminant Analysis. Significantly improved accuracy to approximate levels of 96.0% was established at Decision Tree synthesis. Strongly satisfactory performance indices were reached at the diagnosis of voice profiles using Feed-Forward and Probabilistic Neural Networks, respectively, 98.00% and 100.00%.
Institution: Technical University of Gabrovo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4401460960